Raghunathan, Aditi

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

An Explanation of In-context Learning as Implicit Bayesian Inference

Xie, S. M., Raghunathan, A., Liang, P., & Ma, T. (2021) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 138

Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

Miller, J., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., & Schmidt, L. (2021) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 74

ImpossibleBench: Measuring LLMs' Propensity of Exploiting Test Cases

Zhong, Z., Raghunathan, A., & Carlini, N. (2025) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 54

Testing the Limits of Jailbreaking Defenses with the Purple Problem

Kim, T., Kotha, S., & Raghunathan, A. (2024) · arXiv

Adversarial Robustness in Machine LearningHate Speech and Cyberbullying Detection

Cited by 20

Agreement-On-The-Line: Predicting the Performance of Neural Networks Under Distribution Shift

Baek, C., Jiang, Y., Raghunathan, A., & Kolter, J. Z. (2022) · Advances in Neural Information Processing Systems 35

Domain Adaptation and Few-Shot LearningNeural Networks and Applications

Cited by 20

Understanding Finetuning for Factual Knowledge Extraction

Ghosal, G., Hashimoto, T., & Raghunathan, A. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 3

Dissecting Adversarial Robustness of Multimodal LM Agents

Wu, C. H., Shah, R., Koh, J. Y., Salakhutdinov, R., Fried, D., & Raghunathan, A. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 1